Supervised learning
Supervised learning is about observing or directing the execution of something. The input that's given to the model is the prediction we want to make. The labeled data is the explicit prediction given for the particular instances of the input. Supervised learning requires labeled data, which requires some expertise. However, these conditions are not always met. We don't always posses the labeled dataset. For example, fraud prediction is one of the rapidly unfolding fields where the attacker is constantly looking for available exploits. These new attacks can't possibly be maintained under a dataset with labelled attacks.
Mathematically, the mapping functions of the input to the output can be expressed as Y = f(X). Here, ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access